remotesensing-logo

Journal Browser

Journal Browser

Current Advances in Radar Technologies for Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1512

Special Issue Editor


E-Mail Website1 Website2
Guest Editor
Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
Interests: radar signal processing; passive radars; space surveillance radars; adaptive signal processing; biomedical signal processing; machine learning; analog-to-digital converters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar technologies serve as the cornerstone of most remote sensing systems, driving innovation and unlocking new frontiers in modern remote sensing applications. It is imperative for the remote sensing community to stay abreast with the latest breakthroughs in radar technologies, particularly those poised to shape future advancements.

With this in mind, we are delighted to announce a Special Issue dedicated to showcasing cutting-edge research in radar technologies with significant implications for remote sensing. This Special Issue collects extended versions of papers presented during the International Radar Symposium 2024 (IRS 2024). The conference will be held between 1st and 4th of July 2024 in Wroclaw, Poland. The best contributions from the leading experts in these fields of research will be extended into full journal articles, collected, and presented in this Special Issue. Other researchers and practitioners who will not be able to attend these conferences are also invited to submit their original manuscripts on the topics covered by this Special Issue.

Potential topics of interest include, but are not limited to, the following:

  • Radar Technologies and Techniques for Remote Sensing;
  • Radar Applications in Remote Sensing
  • Airborne Radar
  • Spaceborne Radar
  • Radar Techniques for Space Situational Awareness
  • Automotive and Maritime Radar
  • HF and Over-the-Horizon Radar
  • Weather Radar
  • MIMO Radar
  • UWB and Noise Radar
  • Millimeter Wave and THz Radar
  • Cognitive Radar
  • Multi-Channel and Array Processing
  • Adaptive Signal Processing / STAP
  • SAR / ISAR Imaging
  • Compressive Sensing
  • Localization and Tracking
  • Ground Moving Target Indication
  • Sensor Data Fusion
  • Passive, Bistatic, and Multi-Static Radar
  • Forward Scattering Radar
  • Antennas, Arrays, and Beamforming
  • Propagation of Radar Signals
  • Polarimetric Radar / Radar Polarimetry
  • Radar and Clutter Modelling

Prof. Dr. Konrad Jędrzejewski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radar for remote sensing
  • bistatic/multi-static radar
  • passive radar
  • SAR/ISAR imaging
  • space surveillance
  • satellite applications
  • radar networks

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

24 pages, 28352 KiB  
Article
Accelerating Deep Learning in Radar Systems: A Simulation Framework for 60 GHz Indoor Radar
by Philipp Reitz, Timo Maiwald, Jonas Bönsch, Norman Franchi and Maximilian Lübke
Remote Sens. 2024, 16(21), 4028; https://doi.org/10.3390/rs16214028 - 30 Oct 2024
Viewed by 881
Abstract
FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for [...] Read more.
FMCW radar systems are increasingly used in diverse applications, and emerging technologies like JCAS offer new opportunities. However, machine learning for radar faces challenges due to limited application-specific datasets, often requiring advanced simulations to supplement real-world data. This paper presents a setup for generating synthetic radar data for indoor environments, evaluated using CNNs. The setup involves comprehensive modeling, including far-field antenna simulations, variations in human radar cross-section, and detailed representations of indoor environments with their corresponding propagation channel properties. These synthetic data are used to train CNNs, and their performance is assessed on real measurement data. The results demonstrate that CNNs trained on synthetic data can perform well when tested on real measurement data. Specifically, the models trained with synthetic data showed performance comparable to models trained with real measurement data, which required a minimum of 300 samples to reach similar levels of accuracy. This result demonstrates that synthetic data can effectively train neural networks, providing an alternative to real measurement data, particularly when collecting sufficient real-world samples is difficult or costly. This approach significantly reduces the time required for generating datasets, and the ability to quickly label data in simulations simplifies and accelerates post-processing. Additionally, the generated datasets can be made more heterogeneous by introducing varying signal conditions, enhancing the diversity and robustness of the training data. Full article
(This article belongs to the Special Issue Current Advances in Radar Technologies for Remote Sensing)
Show Figures

Graphical abstract

Back to TopTop